Jakob von raumer
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researchseminars
MathSEE Research Seminars

Visit one of our research seminars for latest results and updates on applications of mathematical methods

Upcoming and Past Seminars
PhD Seminar
KIT Graduate School Computational & Data Science

Get to know KCDS!

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"Mathematics in Sciences, Engineering, and Economics"

The KIT Center "MathSEE" (Mathematics in Sciences, Engineering, and Economics) pools the interdisciplinary mathematical research at KIT since October 2018. The Collaborative Research Center 1173 "Wave Phenomena: Analysis and Numerics" and other existing cooperations form the basis for the establishment of MathSEE. Our members from the career level doctoral researcher onwards work together in exchange formats and interdisciplinary research projects structured in Method Areas. MathSEED through its umbrella graduate school offers a comprehensive program for early career researchers and master students to foster interdisciplinary training. Our graduate school KCDS provides structured program for doctoral students in computational and data science. MathSEE offers to strengthen interdisciplinary mathematical research at KIT and its visibility.

"News from KIT-Center MathSEE"

 

DFG RU
New DFG Research Unit

Asset allocation and asset pricing in regulated markets and institutions is one of 7 research units that was awarded funding by the German Research Foundation, on July 2nd, 2025, for the first funding period. With MathSEE steering board member, Prof. Dr. Nicole Bäuerle, as co-speaker of the research unit and Prof. Dr. Melanie Schienle as PI, we are very pleased and honored to make this announcement. The research unit comprises of 6 institutions including University of Ulm, Karlsruhe Institute of Technology, University of Munster, University of Tübingen, University of Duisburg-Essen and University of Paderborn. 

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krannichkrannich
ERC starting grant

JProf.  Manuel Krannich received the coveted ERC Starting Grant for “Manifolds and Functor Calculus” at the Institute for Algebra and Geometry for a period of five years (2026–2031). The research project will be funded with €1.5 million and is another success for interdisciplinary mathematical research at KIT. We warmly congratulate on the great success!

Profile-JProf Manuel Krannich
berlin science week
AI – Past, Present & Future

What role can AI play not only in modernizing the current society and impacting the global future through innovations but also in understanding history, culture and civilizations will be explored at the Berlin Science Week in a panel discussion on November 5th, 2025 with MathSEE member Prof. Nadja Klein. 

Berlin Science Week
KLein Lab
BMBF project | Flexible, resilient and efficient Machine-Learning-Models

In this project, researchers are developing a general causal foundation model, including high-dimensional, temporal and multimodal data using tolls from representation learning, statistical efficiency theory
and specific ML methods. To enhance efficiency, techniques for efficient learning algorithms specifically tailored to causal machine learning are being investigated, such as synthetic pre-training, transfer learning, and few-shot learning.

BMBF Publication Link
modellansatz
Podcast Modellansatz | Bayesian Learning

Gudrun Thäter talks to Nadja Klein and Moussa Kassem Sbeyti on mathematical method development at the intersection of statistics and machine learning, in particular on Bayesian methods, which allows the incorporation of prior knowledge, quantification of uncertainties bringing insights into the black boxes of machine learning

Modellansatz
AI explainable podcastcampus podcast
Explainable AI - Podcast

With their research on explainable AI, Prof. Nadja Klein and Tim Bündert aim at solving the black box problem of how AI models work and what goes on in the background

KIT Podcast: Campus Report
Mitchell Prize 2025
Mitchell Prize 2024

Prof. Nadja Klein and Clara Hoffmann were awarded the prestigious Mitchell Prize 2024 for their paper titled "Marginally calibrated response distributions for end-to-end learning in autonomous driving" by the Inernational Society of Bayesian Analysis (ISBA). The Mitchell Prize is awarded in recognition of an outstanding paper that describes how a Bayesian analysis has solved an important applied problem. Warmest congratualtions to the MathSEE members.

Mitchell Prize
MathSEE in Townhall
MathSEE in the Karlsruhe townhall

A very successful and engaging "KIT im Rathaus" concluded on July 16th, 2025 with 4 insightful talks, 12 posters and an exhibit. Organized in cooperation with FORUM/KIT, the event was attended by 165 visitors on the evening of July 14th, 2025. For program & details, please visit our webpage. Image-Copyright: FORUM/Grünschloss.

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mathsee-south korea
MathSEE research cooperations with South Korea

On July 11th, Distinguished Prof Byeong Park from Seoul National University and Prof Young Kyung Park from Kangwon National University, Seoul visited KIT Center MathSEE to discuss cooperation opportunities. 

MathSEE Events

Semesterkolloquium WS 25/26

Semesterkolloquium WS 25/26

February 16, 2026InformatiKOM I Geb. 50.19, Adenauerring 12 76131 Karlsruhe
das KIT-Fakultät für Informatik lädt Sie herzlich zum Semesterkolloquium am 16. Februar 2026 um 17:30 Uhr im InformatiKOM 1 ein.
 
Zu Beginn wird der Dekan der Fakultät einen kurzen Überblick über aktuelle Entwicklungen und Perspektiven geben. Im Mittelpunkt der Veranstaltung stehen anschließend die Antrittsvorlesungen von Professorin Nadja Klein und Professor Henning Meyerhenke, die beide neben ihrer Zugehörigkeit zur Fakultät auch am Scientific Computing Center (SCC) tätig sind.
In ihrer Antrittsvorlesung „Bayesian Statistics and Machine Learning: Leveraging the Best of Both Worlds“ stellt Nadja Klein ausgewählte Arbeiten zu Bayesianischem Lernen vor, welches es ermöglicht, Vorwissen mit Daten zu verknüpfen, Unsicherheit zu quantifizieren und die Transparenz moderner Machine‑Learning‑Systeme zu erhöhen. Durch die Integration von Expertenwissen, strukturellen Annahmen oder Regularisierungsverfahren können Bayesianische Methoden existierende Modelle präziser, robuster und daten­effizienter machen und damit zentrale Einschränkungen von Black‑Box‑Ansätzen adressieren. Die Forschung von Nadja Klein verbindet theoretische Arbeiten, methodische Innovationen und praktische Anwendungen. Dazu gehören Beiträge zu räumlicher Statistik, sparsamen und skalierbaren Bayesianischen Modellen und neuronalen Netzen sowie Methoden zur Interpretierbarkeit und Erklärbarkeit komplexer Systeme. Anwendungsseitig arbeitet ihre Gruppe interdisziplinär – von der Analyse komplexer biomedizinischer und Neuroimaging‑Daten über die Vorhersage von Wetter‑ und Umweltmustern bis hin zur Verbesserung autonomer Fahrsysteme. Dieser Vortrag hebt ausgewählte aktuelle methodische Entwicklungen aus ihrer Gruppe hervor und zeigt anhand konkreter Anwendungen, wie Bayesianische Konzepte moderne Machine‑Learning‑Pipelines verbessern können.
 
In der Antrittsvorlesung „Graph Algorithms for Large Complex Systems“ widmet sich Henning Meyerhenke Forschungsfragen, die sich aus sehr großen Netzwerken verschiedener Anwendungsbereiche ergeben. Der Vortrag stellt aktuelle algorithmische Resultate vor, die Probleme aus der Netzwerkanalyse, dem CO2-armen Workflow-Scheduling und der Robustheits-Optimierung von Graphen lösen.
Im Anschluss an das Kolloquium laden wir herzlich zu einem kleinen Empfang ein, der Gelegenheit zum fachlichen aber natürlich auch persönlichen Austausch bietet.
  *der Dekansbericht wird auf Deutsch, die Antrittsvorlesungen in englischer Sprache stattfinden
KIT-Fakultät für Informatik

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